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Targeted Maximum Likelihood Learning

Citations

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Cited by:

  1. Jayne Byakika-Tusiime & Eric C Polley & Jessica H Oyugi & David R Bangsberg, 2013. "Free HIV Antiretroviral Therapy Enhances Adherence among Individuals on Stable Treatment: Implications for Potential Shortfalls in Free Antiretroviral Therapy," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-6, September.
  2. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
  3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
  4. Stitelman Ori M & van der Laan Mark J., 2010. "Collaborative Targeted Maximum Likelihood for Time to Event Data," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-46, June.
  5. Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
  6. Gruber Susan & van der Laan Mark J., 2012. "Targeted Minimum Loss Based Estimator that Outperforms a given Estimator," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-22, May.
  7. Youmi Suk & Jee-Seon Kim & Hyunseung Kang, 2021. "Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 323-347, June.
  8. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
  9. Trevor Fitzpatrick & Seamus Kelly & Patrick Carey & David Walsh & Ruairi Nugent, 2025. "Assessing Generative AI value in a public sector context: evidence from a field experiment," Papers 2502.09479, arXiv.org.
  10. Wang, Hui & Rose, Sherri & van der Laan, Mark J., 2011. "Finding quantitative trait loci genes with collaborative targeted maximum likelihood learning," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 792-796, July.
  11. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
  12. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
  13. Hannah H Leslie & Deborah A Karasek & Laura F Harris & Emily Chang & Naila Abdulrahim & May Maloba & Megan J Huchko, 2014. "Cervical Cancer Precursors and Hormonal Contraceptive Use in HIV-Positive Women: Application of a Causal Model and Semi-Parametric Estimation Methods," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-8, June.
  14. Guanbo Wang & Mireille E. Schnitzer & Dick Menzies & Piret Viiklepp & Timothy H. Holtz & Andrea Benedetti, 2020. "Estimating treatment importance in multidrug‐resistant tuberculosis using Targeted Learning: An observational individual patient data network meta‐analysis," Biometrics, The International Biometric Society, vol. 76(3), pages 1007-1016, September.
  15. Stephens Alisa & Tchetgen Tchetgen Eric & De Gruttola Victor, 2014. "Locally Efficient Estimation of Marginal Treatment Effects When Outcomes Are Correlated: Is the Prize Worth the Chase?," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 59-75, May.
  16. Christian Gische & Manuel C. Voelkle, 2022. "Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 868-901, September.
  17. van der Laan Mark J., 2010. "Targeted Maximum Likelihood Based Causal Inference: Part I," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-45, February.
  18. Rose Sherri & van der Laan Mark J., 2011. "A Targeted Maximum Likelihood Estimator for Two-Stage Designs," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, March.
  19. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
  20. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
  21. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
  22. Stitelman Ori M. & De Gruttola Victor & van der Laan Mark J., 2012. "A General Implementation of TMLE for Longitudinal Data Applied to Causal Inference in Survival Analysis," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-39, September.
  23. Antoine Chambaz & Mark J. Laan, 2014. "Inference in Targeted Group-Sequential Covariate-Adjusted Randomized Clinical Trials," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 104-140, March.
  24. Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
  25. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
  26. Zheng Wenjing & van der Laan Mark J., 2012. "Targeted Maximum Likelihood Estimation of Natural Direct Effects," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-40, January.
  27. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
  28. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
  29. Jenna Wong & Daniel Prieto-Alhambra & Peter R. Rijnbeek & Rishi J. Desai & Jenna M. Reps & Sengwee Toh, 2022. "Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations," Drug Safety, Springer, vol. 45(5), pages 493-510, May.
  30. Hubbard Alan E. & Kherad-Pajouh Sara & van der Laan Mark J., 2016. "Statistical Inference for Data Adaptive Target Parameters," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 3-19, May.
  31. Maria Kamran, 2022. "A Touch of Violence - Welfare Outcomes under Bride Exchange and Child Brides," IHEID Working Papers 12-2022, Economics Section, The Graduate Institute of International Studies.
  32. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  33. Bezawit Adugna Bahru & Manfred Zeller, 2022. "Gauging the impact of Ethiopia’s productive safety net programme on agriculture: Application of targeted maximum likelihood estimation approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 257-276, February.
  34. Susan Gruber & Mark J. van der Laan, 2013. "An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data," Biometrics, The International Biometric Society, vol. 69(1), pages 254-262, March.
  35. Sourabh Balgi & Adel Daoud & Jose M. Pe~na & Geoffrey T. Wodtke & Jesse Zhou, 2024. "Deep Learning With DAGs," Papers 2401.06864, arXiv.org.
  36. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
  37. Geeven Geert & van der Laan Mark J. & de Gunst Mathisca C.M., 2012. "Comparison of Targeted Maximum Likelihood and Shrinkage Estimators of Parameters in Gene Networks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-29, September.
  38. Zheng Wenjing & Petersen Maya & van der Laan Mark J., 2016. "Doubly Robust and Efficient Estimation of Marginal Structural Models for the Hazard Function," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 233-252, May.
  39. Jun Wang & Yahe Yu, 2024. "Improved estimation of average treatment effects under covariate‐adaptive randomization methods," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(2), pages 310-333, May.
  40. Stijn Vansteelandt & Oliver Dukes, 2022. "Assumption‐lean inference for generalised linear model parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 657-685, July.
  41. Brooks Jordan & van der Laan Mark J. & Go Alan S., 2012. "Targeted Maximum Likelihood Estimation for Prediction Calibration," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-35, October.
  42. Jeremiah Jones & Ashkan Ertefaie & Susan M. Shortreed, 2023. "Rejoinder to “Reader reaction to ‘Outcome‐adaptive Lasso: Variable selection for causal inference’ by Shortreed and Ertefaie (2017)”," Biometrics, The International Biometric Society, vol. 79(1), pages 521-525, March.
  43. Veronica Sciannameo & Gian Paolo Fadini & Daniele Bottigliengo & Angelo Avogaro & Ileana Baldi & Dario Gregori & Paola Berchialla, 2022. "Assessment of Glucose Lowering Medications’ Effectiveness for Cardiovascular Clinical Risk Management of Real-World Patients with Type 2 Diabetes: Targeted Maximum Likelihood Estimation under Model Mi," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
  44. Mishra, Sarba Narayan & Kumar, K. Nirmal Ravi & Reddy, M. Jagan Mohan & Nandy, A. & Mandal, B.K. & Mishra, S. & Das, M. K., 2023. "A Study on the Adoption and Impact of Finger Millet Landrace (Bada Mandia) in Koraput District of Odisha," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 0(Number 3), September.
  45. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
  46. Yebin Tao & Lu Wang, 2017. "Adaptive contrast weighted learning for multi-stage multi-treatment decision-making," Biometrics, The International Biometric Society, vol. 73(1), pages 145-155, March.
  47. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
  48. Chaffee Paul H. & van der Laan Mark J., 2012. "Targeted Maximum Likelihood Estimation for Dynamic Treatment Regimes in Sequentially Randomized Controlled Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-32, June.
  49. Ziyun Xu & Éric Archambault, 2015. "Chinese interpreting studies: structural determinants of MA students’ career choices," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1041-1058, November.
  50. Antonio R. Linero, 2023. "Prior and posterior checking of implicit causal assumptions," Biometrics, The International Biometric Society, vol. 79(4), pages 3153-3164, December.
  51. Hai Zhu & Hongjian Zhu, 2023. "Covariate‐adjusted response‐adaptive designs based on semiparametric approaches," Biometrics, The International Biometric Society, vol. 79(4), pages 2895-2906, December.
  52. Aaron L. Sarvet & Kerollos N. Wanis & Jessica G. Young & Roberto Hernandez‐Alejandro & Mats J. Stensrud, 2023. "Longitudinal incremental propensity score interventions for limited resource settings," Biometrics, The International Biometric Society, vol. 79(4), pages 3418-3430, December.
  53. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
  54. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
  55. Yunda Huang & Lily Zhang & Shelly Karuna & Philip Andrew & Michal Juraska & Joshua A. Weiner & Heather Angier & Evgenii Morgan & Yasmin Azzam & Edith Swann & Srilatha Edupuganti & Nyaradzo M. Mgodi & , 2023. "Adults on pre-exposure prophylaxis (tenofovir-emtricitabine) have faster clearance of anti-HIV monoclonal antibody VRC01," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
  56. van der Laan Mark J. & Gruber Susan, 2010. "Collaborative Double Robust Targeted Maximum Likelihood Estimation," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-71, May.
  57. Sun Hao & Ertefaie Ashkan & Lu Xin & Johnson Brent A., 2020. "Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 300-314, January.
  58. Simeone Marino & Yi Zhao & Nina Zhou & Yiwang Zhou & Arthur W Toga & Lu Zhao & Yingsi Jian & Yichen Yang & Yehu Chen & Qiucheng Wu & Jessica Wild & Brandon Cummings & Ivo D Dinov, 2020. "Compressive Big Data Analytics: An ensemble meta-algorithm for high-dimensional multisource datasets," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
  59. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
  60. Guo, Xu & Fang, Yun & Zhu, Xuehu & Xu, Wangli & Zhu, Lixing, 2018. "Semiparametric double robust and efficient estimation for mean functionals with response missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 325-339.
  61. Torben Martinussen & Mats Julius Stensrud, 2023. "Estimation of separable direct and indirect effects in continuous time," Biometrics, The International Biometric Society, vol. 79(1), pages 127-139, March.
  62. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
  63. Levy Jonathan & van der Laan Mark & Hubbard Alan & Pirracchio Romain, 2021. "A fundamental measure of treatment effect heterogeneity," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 83-108, January.
  64. Huaiyu Zang & Hang J. Kim & Bin Huang & Rhonda Szczesniak, 2023. "Bayesian causal inference for observational studies with missingness in covariates and outcomes," Biometrics, The International Biometric Society, vol. 79(4), pages 3624-3636, December.
  65. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
  66. Rahul Singh & Liyuan Xu & Arthur Gretton, 2021. "Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves," Papers 2111.03950, arXiv.org, revised Mar 2025.
  67. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
  68. Jianxuan Liu & Yanyuan Ma & Lan Wang, 2018. "An alternative robust estimator of average treatment effect in causal inference," Biometrics, The International Biometric Society, vol. 74(3), pages 910-923, September.
  69. Jenny Häggström, 2018. "Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 389-398, June.
  70. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
  71. Iván Díaz & Elizabeth Colantuoni & Daniel F. Hanley & Michael Rosenblum, 2019. "Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 439-468, July.
  72. Elise D Riley & Torsten B Neilands & Kelly Moore & Jennifer Cohen & David R Bangsberg & Diane Havlir, 2012. "Social, Structural and Behavioral Determinants of Overall Health Status in a Cohort of Homeless and Unstably Housed HIV-Infected Men," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
  73. Gruber Susan & van der Laan Mark J., 2010. "A Targeted Maximum Likelihood Estimator of a Causal Effect on a Bounded Continuous Outcome," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-18, August.
  74. Zhiwei Zhang & Richard M. Kotz & Chenguang Wang & Shiling Ruan & Martin Ho, 2013. "A Causal Model for Joint Evaluation of Placebo and Treatment-Specific Effects in Clinical Trials," Biometrics, The International Biometric Society, vol. 69(2), pages 318-327, June.
  75. Haight, Thaddeus J. & Wang, Yue & van der Laan, Mark J. & Tager, Ira B., 2010. "A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3080-3094, December.
  76. Helmut Farbmacher & Martin Huber & Lukáš Lafférs & Henrika Langen & Martin Spindler, 2022. "Causal mediation analysis with double machine learning [Mediation analysis via potential outcomes models]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 277-300.
  77. Díaz Muñoz Iván & van der Laan Mark J., 2011. "Super Learner Based Conditional Density Estimation with Application to Marginal Structural Models," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-20, October.
  78. Daniel Scharfstein & Aidan McDermott & Iván Díaz & Marco Carone & Nicola Lunardon & Ibrahim Turkoz, 2018. "Global sensitivity analysis for repeated measures studies with informative drop†out: A semi†parametric approach," Biometrics, The International Biometric Society, vol. 74(1), pages 207-219, March.
  79. Mireille E. Schnitzer & Erica E.M. Moodie & Mark J. van der Laan & Robert W. Platt & Marina B. Klein, 2014. "Modeling the impact of hepatitis C viral clearance on end-stage liver disease in an HIV co-infected cohort with targeted maximum likelihood estimation," Biometrics, The International Biometric Society, vol. 70(1), pages 144-152, March.
  80. Chaudhuri, Saraswata & Renault, Eric, 2023. "Efficient estimation of regression models with user-specified parametric model for heteroskedasticty," The Warwick Economics Research Paper Series (TWERPS) 1473, University of Warwick, Department of Economics.
  81. Kara E. Rudolph & Jonathan Levy & Mark J. van der Laan, 2021. "Transporting stochastic direct and indirect effects to new populations," Biometrics, The International Biometric Society, vol. 77(1), pages 197-211, March.
  82. Kara E. Rudolph & Mark J. Laan, 2017. "Robust estimation of encouragement design intervention effects transported across sites," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1509-1525, November.
  83. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
  84. Hubbard Alan & Jamshidian Farid & Jewell Nicholas, 2012. "Adjusting for Perception and Unmasking Effects in Longitudinal Clinical Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-20, December.
  85. S Ariane Christie & Amanda S Conroy & Rachael A Callcut & Alan E Hubbard & Mitchell J Cohen, 2019. "Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.
  86. Stitelman Ori M & Wester C. William & De Gruttola Victor & van der Laan Mark J., 2011. "Targeted Maximum Likelihood Estimation of Effect Modification Parameters in Survival Analysis," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-34, March.
  87. Chambaz Antoine & van der Laan Mark J., 2011. "Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Simulation Study," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-30, January.
  88. Joseph Antonelli & Georgia Papadogeorgou & Francesca Dominici, 2022. "Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties," Biometrics, The International Biometric Society, vol. 78(1), pages 100-114, March.
  89. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
  90. Philipp Schwarz & Oliver Schacht & Sven Klaassen & Daniel Grunbaum & Sebastian Imhof & Martin Spindler, 2024. "Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?," Papers 2406.11308, arXiv.org.
  91. Rahul Singh, 2021. "Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection," Papers 2111.05277, arXiv.org.
  92. Iván Díaz & Alan Hubbard & Anna Decker & Mitchell Cohen, 2015. "Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
  93. Gruber Susan & van der Laan Mark J., 2010. "An Application of Collaborative Targeted Maximum Likelihood Estimation in Causal Inference and Genomics," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-31, May.
  94. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
  95. Rose Sherri & van der Laan Mark J., 2008. "Simple Optimal Weighting of Cases and Controls in Case-Control Studies," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-26, September.
  96. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
  97. repec:bla:istatr:v:83:y:2015:i:3:p:449-471 is not listed on IDEAS
  98. Li, Li & Shi, Pengfei & Fan, Qingliang & Zhong, Wei, 2024. "Causal effect estimation with censored outcome and covariate selection," Statistics & Probability Letters, Elsevier, vol. 204(C).
  99. Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2022. "Estimation of Conditional Average Treatment Effects With High-Dimensional Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 313-327, January.
  100. Sherri Rose & Sharon‐Lise Normand, 2019. "Double robust estimation for multiple unordered treatments and clustered observations: Evaluating drug‐eluting coronary artery stents," Biometrics, The International Biometric Society, vol. 75(1), pages 289-296, March.
  101. van der Laan Mark J. & Petersen Maya & Zheng Wenjing, 2013. "Estimating the Effect of a Community-Based Intervention with Two Communities," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 83-106, June.
  102. Wei Luo & Yeying Zhu & Debashis Ghosh, 2017. "On estimating regression-based causal effects using sufficient dimension reduction," Biometrika, Biometrika Trust, vol. 104(1), pages 51-65.
  103. David Cheng & Ashwin N. Ananthakrishnan & Tianxi Cai, 2021. "Robust and efficient semi‐supervised estimation of average treatment effects with application to electronic health records data," Biometrics, The International Biometric Society, vol. 77(2), pages 413-423, June.
  104. Antonelli Joseph & Cefalu Matthew, 2020. "Averaging causal estimators in high dimensions," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 92-107, January.
  105. Thai T. Pham & Yuanyuan Shen, 2017. "A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform," Papers 1706.02795, arXiv.org.
  106. Hugo Bodory & Martin Huber & Lukáš Lafférs, 2022. "Evaluating (weighted) dynamic treatment effects by double machine learning [Identification of causal effects using instrumental variables]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 628-648.
  107. Zelin Zhang & Kejia Yang & Jonathan Z. Zhang & Robert W. Palmatier, 2023. "Uncovering Synergy and Dysergy in Consumer Reviews: A Machine Learning Approach," Management Science, INFORMS, vol. 69(4), pages 2339-2360, April.
  108. Michael Rosenblum & Nicholas P. Jewell & Mark van der Laan & Stephen Shiboski & Ariane van der Straten & Nancy Padian, 2009. "Analysing direct effects in randomized trials with secondary interventions: an application to human immunodeficiency virus prevention trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 443-465, April.
  109. Iván Díaz Muñoz & Mark van der Laan, 2012. "Population Intervention Causal Effects Based on Stochastic Interventions," Biometrics, The International Biometric Society, vol. 68(2), pages 541-549, June.
  110. Anish Agarwal & Rahul Singh, 2021. "Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy," Papers 2107.02780, arXiv.org, revised Feb 2024.
  111. Anders Munch & Marie Skov Breum & Torben Martinussen & Thomas A. Gerds, 2023. "Targeted estimation of state occupation probabilities for the non‐Markov illness‐death model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 1532-1551, September.
  112. Chambaz Antoine & van der Laan Mark J., 2011. "Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Theoretical Study," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-32, January.
  113. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
  114. Tuglus Catherine & van der Laan Mark J., 2011. "Repeated Measures Semiparametric Regression Using Targeted Maximum Likelihood Methodology with Application to Transcription Factor Activity Discovery," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-31, January.
  115. Zhiwei Zhang & Zhen Chen & James F. Troendle & Jun Zhang, 2012. "Causal Inference on Quantiles with an Obstetric Application," Biometrics, The International Biometric Society, vol. 68(3), pages 697-706, September.
  116. David Benkeser & Keith Horvath & Cathy J. Reback & Joshua Rusow & Michael Hudgens, 2020. "Design and Analysis Considerations for a Sequentially Randomized HIV Prevention Trial," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 446-467, December.
  117. Kasy Maximilian, 2009. "Semiparametrically Efficient Estimation of Conditional Instrumental Variables Parameters," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-25, June.
  118. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.
  119. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
  120. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
  121. Tuglus Catherine & van der Laan Mark J., 2009. "Modified FDR Controlling Procedure for Multi-Stage Analyses," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-17, February.
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